Ocean Acidification: pH#

../../../_images/7ebf80348a4bbd7501e1736993ff884e56bc13941267f3bbe7e3000ecb795466.png

Figure. Change in pH from hindcast. The map (top) shows the change in mean surface pH in the vicinity of Palau over the period 1993- 2022. The grey line is the Palau EEZ. The line plot (bottom) shows the change in mean surface pH averaged over the area within the top plot. The solid black line represents the trend, which is statistically significant (p < 0.05). The colored dots represent the 10 years with the lowest pH on record.

Hide code cell source
import warnings
warnings.filterwarnings("ignore")
import os
import os.path as op
import sys

import pandas as pd
import numpy as np
import xarray as xr
import geopandas as gpd
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
from myst_nb import glue 

sys.path.append("../../../../indicators_setup")

from ind_setup.plotting_int import plot_timeseries_interactive, plot_oni_index_th
from ind_setup.plotting import plot_base_map, plot_map_subplots, add_oni_cat, plot_bar_probs, fontsize


sys.path.append("../../../functions")
from data_downloaders import download_oni_index

Setup#

Define area of interest

#Area of interest
lon_range  = [129.4088, 137.0541]
lat_range = [1.5214, 11.6587]

EEZ shapefile

path_figs = "../../../matrix_cc/figures"
shp_f = op.join(os.getcwd(), '..', '..','..', 'data/Palau_EEZ/pw_eez_pol_april2022.shp')
shp_eez = gpd.read_file(shp_f)

Load Data#

data_xr = xr.open_dataset(op.join(os.getcwd(), '..', '..','..', 'data/data_phyc_o2_ph.nc')).isel(depth = 0)
dataset_id = 'ph'
label = 'pH'

Analysis#

Plotting#

Average#

fig, ax = plot_base_map(shp_eez = shp_eez, figsize = [10, 6])
im = ax.pcolor(data_xr.longitude, data_xr.latitude, data_xr.mean(dim='time')[dataset_id], transform=ccrs.PlateCarree(), 
                cmap = 'magma_r', 
                vmin = np.nanpercentile(data_xr.mean(dim = 'time')[dataset_id], 1), 
                vmax = np.nanpercentile(data_xr.mean(dim = 'time')[dataset_id], 99))
ax.set_extent([lon_range[0], lon_range[1], lat_range[0], lat_range[1]], crs=ccrs.PlateCarree())
plt.colorbar(im, ax=ax, label= label)
glue("average_map", fig, display=False)
plt.savefig(op.join(path_figs, 'F14_pH_mean_map.png'), dpi=300, bbox_inches='tight')
../../../_images/7ebf80348a4bbd7501e1736993ff884e56bc13941267f3bbe7e3000ecb795466.png

Annual average#

data_y = data_xr.resample(time='1YE').mean()
im = plot_map_subplots(data_y, dataset_id, shp_eez = shp_eez, cmap = 'magma_r', 
                  vmin = np.nanpercentile(data_xr.min(dim = 'time')[dataset_id], 1), 
                  vmax = np.nanpercentile(data_xr.max(dim = 'time')[dataset_id], 99),
                  cbar = 1)
../../../_images/6cb145442ba302e5484dac01739b6fdbeac8467b7e26b107401918781a09bd66.png

Annual anomaly#

data_an = data_y - data_xr.mean(dim='time')
fig = plot_map_subplots(data_an, dataset_id, shp_eez = shp_eez, cmap='RdBu_r', vmin=-.1, vmax=.1, cbar = 1)
../../../_images/33ac1a205621971dc0300ed11c8e951d8507b8619a4d11459f23bcf56ecb6452.png

Average over area#

dict_plot = [{'data' : data_xr.mean(dim = ['longitude', 'latitude']).to_dataframe(), 
              'var' : dataset_id, 'ax' : 1, 'label' : 'pH - MEAN AREA'},]
fig = plot_timeseries_interactive(dict_plot, trendline=True, scatter_dict = None, figsize = (25, 12));
fig.write_html(op.join(path_figs, 'F14_pH_mean_trend.html'), include_plotlyjs="cdn")

Timeseries at a given point#

loc = [7.37, 134.7]
dict_plot = [{'data' : data_xr.sel(longitude=loc[1], latitude=loc[0], method='nearest').to_dataframe(), 
              'var' : dataset_id, 'ax' : 1, 'label' : f'{label} at [{loc[0]}, {loc[1]}]'},]
fig, ax = plot_base_map(shp_eez = shp_eez, figsize = [10, 6])
ax.set_extent([lon_range[0], lon_range[1], lat_range[0], lat_range[1]], crs=ccrs.PlateCarree())
ax.plot(loc[1], loc[0], '*', markersize = 12, color = 'royalblue', transform=ccrs.PlateCarree(), label = 'Location Analysis')
ax.legend()
<matplotlib.legend.Legend at 0x1812e0080>
../../../_images/038a2757803511e8b284fa11a57d0ba084269dbabe56e09215299e69572e231d.png
fig = plot_timeseries_interactive(dict_plot, trendline=True, scatter_dict = None, figsize = (25, 12));

ONI index analysis#

p_data = 'https://psl.noaa.gov/data/correlation/oni.data'
df1 = download_oni_index(p_data)
lims = [-.5, .5]
plot_oni_index_th(df1, lims = lims)

Group by ONI category

df1 = add_oni_cat(df1, lims = lims)
df1['ONI'] = df1['oni_cat']
data_xr['ONI'] = (('time'), df1.iloc[np.intersect1d(data_xr.time, df1.index, return_indices=True)[2]].ONI.values)
data_xr['ONI_cat'] = (('time'), np.where(data_xr.ONI < lims[0], -1, np.where(data_xr.ONI > lims[1], 1, 0)))
data_oni = data_xr.groupby('ONI_cat').mean()

Average#

fig = plot_map_subplots(data_oni, dataset_id, shp_eez = shp_eez, cmap = 'magma_r', 
                  vmin = np.nanpercentile(data_xr.mean(dim = 'time')[dataset_id], 1)-.005, 
                  vmax = np.nanpercentile(data_xr.mean(dim = 'time')[dataset_id], 99) + .005,
                  sub_plot= [1, 3], figsize = (20, 9), cbar = True, cbar_pad = 0.1,
                  titles = ['La Niña', 'Neutral', 'El Niño'],)

plt.savefig(op.join(path_figs, 'F14_pH_ENSO.png'), dpi=300, bbox_inches='tight')
../../../_images/2c21d234bb381e4363d45b1df67d321ee8e3d7e0a344cbabb57b72b7db2f1d00.png

Anomaly#

data_an = data_oni - data_xr.mean(dim='time')
fig = plot_map_subplots(data_an, dataset_id, shp_eez = shp_eez, cmap='RdBu_r', vmin=-.005, vmax=.005,
                  sub_plot= [1, 3], figsize = (20, 9), cbar = True, cbar_pad = 0.1,
                  titles = ['La Niña', 'Neutral', 'El Niño'],)
../../../_images/c25e78bfe0ae7209a234fcf4f584bc0247fe612c4f513507aa4418f042394edc.png